statsmodels.discrete.conditional_models.ConditionalResults#
- class statsmodels.discrete.conditional_models.ConditionalResults(model, params, normalized_cov_params, scale)[source]#
- Attributes:
- bse
The standard errors of the parameter estimates
- llf
Log-likelihood of model
- pvalues
The two-tailed p values for the t-stats of the params
- tvalues
Return the t-statistic for a given parameter estimate
use_tFlag indicating to use the Student’s distribution in inference
Methods
conf_int([alpha, cols])Construct confidence interval for the fitted parameters
cov_params([r_matrix, column, scale, cov_p, ...])Compute the variance/covariance matrix
f_test(r_matrix[, cov_p, invcov])Compute the F-test for a joint linear hypothesis
initialize(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance
load(fname)Load a pickled results instance
See specific model class docstring
predict([exog, transform])Call self.model.predict with self.params as the first argument
Remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])Save a pickle of this instance
summary([yname, xname, title, alpha])Summarize the fitted model.
t_test(r_matrix[, cov_p, use_t])Compute a t-test for each linear hypothesis of the form Rb = q
t_test_pairwise(term_name[, method, alpha, ...])Perform pairwise t_test with multiple testing corrected p-values
wald_test(r_matrix[, cov_p, invcov, use_f, ...])Compute a Wald-test for a joint linear hypothesis
wald_test_terms([skip_single, ...])Compute a sequence of Wald tests for terms over multiple columns
Methods
conf_int([alpha, cols])Construct confidence interval for the fitted parameters
cov_params([r_matrix, column, scale, cov_p, ...])Compute the variance/covariance matrix
f_test(r_matrix[, cov_p, invcov])Compute the F-test for a joint linear hypothesis
initialize(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance
load(fname)Load a pickled results instance
See specific model class docstring
predict([exog, transform])Call self.model.predict with self.params as the first argument
Remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])Save a pickle of this instance
summary([yname, xname, title, alpha])Summarize the fitted model.
t_test(r_matrix[, cov_p, use_t])Compute a t-test for each linear hypothesis of the form Rb = q
t_test_pairwise(term_name[, method, alpha, ...])Perform pairwise t_test with multiple testing corrected p-values
wald_test(r_matrix[, cov_p, invcov, use_f, ...])Compute a Wald-test for a joint linear hypothesis
wald_test_terms([skip_single, ...])Compute a sequence of Wald tests for terms over multiple columns
Properties